OpenRaiser/Intern-Atlas huggingface dataset:
An Analysis of Performance Bottlenecks in MRI Pre-Processing
Venue: arXiv cs.PF 2024
Year: 2024

Magnetic Resonance Image (MRI) pre-processing is a critical step for neuroimaging analysis. However, the computational cost of MRI pre-processing pipelines is a major bottleneck for large cohort studies and some clinical applications. While High-Performance Computing (HPC) and, more recently, Deep Learning have been adopted to accelerate the computations, these techniques require costly hardware and are not accessible to all researchers. Therefore, it is important to understand the performance bottlenecks of MRI pre-processing pipelines to improve their performance. Using Intel VTune profiler, we characterized the bottlenecks of several commonly used MRI-preprocessing pipelines from the ANTs, FSL, and FreeSurfer toolboxes. We found that few functions contributed to most of the CPU time, and that linear interpolation was the largest contributor. Data access was also a substantial bottleneck. We identified a bug in the ITK library that impacts the performance of ANTs pipeline in single-precision and a potential issue with the OpenMP scaling in FreeSurfer recon-all. Our results provide a reference for future efforts to optimize MRI pre-processing pipelines.
AdapNet: Adaptive Noise-Based Network for Low-Quality Image Retrieval
Venue: arXiv cs.CV 2024
Year: 2024

Image retrieval aims to identify visually similar images within a database using a given query image. Traditional methods typically employ both global and local features extracted from images for matching, and may also apply re-ranking techniques to enhance accuracy. However, these methods often fail to account for the noise present in query images, which can stem from natural or human-induced factors, thereby negatively impacting retrieval performance. To mitigate this issue, we introduce a novel setting for low-quality image retrieval, and propose an Adaptive Noise-Based Network (AdapNet) to learn robust abstract representations. Specifically, we devise a quality compensation block trained to compensate for various low-quality factors in input images. Besides, we introduce an innovative adaptive noise-based loss function, which dynamically adjusts its focus on the gradient in accordance with image quality, thereby augmenting the learning of unknown noisy samples during training and enhancing intra-class compactness. To assess the performance, we construct two datasets with low-quality queries, which is built by applying various types of noise on clean query images on the standard Revisited Oxford and Revisited Paris datasets. Comprehensive experimental results illustrate that AdapNet surpasses state-of-the-art methods on the Noise Revisited Oxford and Noise Revisited Paris benchmarks, while maintaining competitive performance on high-quality datasets. The code and constructed datasets will be made available.
C$^{3}$Bench: A Comprehensive Classical Chinese Understanding Benchmark for Large Language Models
Venue: arXiv cs.CL 2024
Year: 2024

Classical Chinese Understanding (CCU) holds significant value in preserving and exploration of the outstanding traditional Chinese culture. Recently, researchers have attempted to leverage the potential of Large Language Models (LLMs) for CCU by capitalizing on their remarkable comprehension and semantic capabilities. However, no comprehensive benchmark is available to assess the CCU capabilities of LLMs. To fill this gap, this paper introduces C$^{3}$bench, a Comprehensive Classical Chinese understanding benchmark, which comprises 50,000 text pairs for five primary CCU tasks, including classification, retrieval, named entity recognition, punctuation, and translation. Furthermore, the data in C$^{3}$bench originates from ten different domains, covering most of the categories in classical Chinese. Leveraging the proposed C$^{3}$bench, we extensively evaluate the quantitative performance of 15 representative LLMs on all five CCU tasks. Our results not only establish a public leaderboard of LLMs' CCU capabilities but also gain some findings. Specifically, existing LLMs are struggle with CCU tasks and still inferior to supervised models. Additionally, the results indicate that CCU is a task that requires special attention. We believe this study could provide a standard benchmark, comprehensive baselines, and valuable insights for the future advancement of LLM-based CCU research. The evaluation pipeline and dataset are available at \url{https://github.com/SCUT-DLVCLab/C3bench}.
RREH: Reconstruction Relations Embedded Hashing for Semi-Paired Cross-Modal Retrieval
Venue: arXiv cs.IR 2024
Year: 2024

Known for efficient computation and easy storage, hashing has been extensively explored in cross-modal retrieval. The majority of current hashing models are predicated on the premise of a direct one-to-one mapping between data points. However, in real practice, data correspondence across modalities may be partially provided. In this research, we introduce an innovative unsupervised hashing technique designed for semi-paired cross-modal retrieval tasks, named Reconstruction Relations Embedded Hashing (RREH). RREH assumes that multi-modal data share a common subspace. For paired data, RREH explores the latent consistent information of heterogeneous modalities by seeking a shared representation. For unpaired data, to effectively capture the latent discriminative features, the high-order relationships between unpaired data and anchors are embedded into the latent subspace, which are computed by efficient linear reconstruction. The anchors are sampled from paired data, which improves the efficiency of hash learning. The RREH trains the underlying features and the binary encodings in a unified framework with high-order reconstruction relations preserved. With the well devised objective function and discrete optimization algorithm, RREH is designed to be scalable, making it suitable for large-scale datasets and facilitating efficient cross-modal retrieval. In the evaluation process, the proposed is tested with partially paired data to establish its superiority over several existing methods.
On the Downlink Average Energy Efficiency of Non-Stationary XL-MIMO
Venue: arXiv cs.IT 2024
Year: 2024

Extra large-scale multiple-input multiple-output (XL-MIMO) is a key technology for future wireless communication systems. This paper considers the effects of visibility region (VR) at the base station (BS) in a non-stationary multi-user XL-MIMO scenario, where only partial antennas can receive users' signal. In time division duplexing (TDD) mode, we first estimate the VR at the BS by detecting the energy of the received signal during uplink training phase. The probabilities of two detection errors are derived and the uplink channel on the detected VR is estimated. In downlink data transmission, to avoid cumbersome Monte-Carlo trials, we derive a deterministic approximate expression for ergodic {average energy efficiency (EE)} with the regularized zero-forcing (RZF) precoding. In frequency division duplexing (FDD) mode, the VR is estimated in uplink training and then the channel information of detected VR is acquired from the feedback channel. In downlink data transmission, the approximation of ergodic average {EE} is also derived with the RZF precoding. Invoking approximate results, we propose an alternate optimization algorithm to design the detection threshold and the pilot length in both TDD and FDD modes. The numerical results reveal the impacts of VR estimation error on ergodic average {EE} and demonstrate the effectiveness of our proposed algorithm.
Bandwidth Efficient Cache Selection and Content Advertisement
Venue: arXiv cs.NI 2024
Year: 2024

Caching is extensively used in various networking environments to optimize performance by reducing latency, bandwidth, and energy consumption. To optimize performance, caches often advertise their content using indicators, which are data structures that trade space efficiency for accuracy. However, this tradeoff introduces the risk of false indications. Existing solutions for cache content advertisement and cache selection often lead to inefficiencies, failing to adapt to dynamic network conditions. This paper introduces SALSA2, a Scalable Adaptive and Learning-based Selection and Advertisement Algorithm, which addresses these limitations through a dynamic and adaptive approach. SALSA2 accurately estimates mis-indication probabilities by considering inter-cache dependencies and dynamically adjusts the size and frequency of indicator advertisements to minimize transmission overhead while maintaining high accuracy. Our extensive simulation study, conducted using a variety of real-world cache traces, demonstrates that SALSA2 achieves up to 84\% bandwidth savings compared to the state-of-the-art solution and close-to-optimal service cost in most scenarios. These results highlight SALSA2's effectiveness in enhancing cache management, making it a robust and versatile solution for modern networking challenges.
The Impacts of Data, Ordering, and Intrinsic Dimensionality on Recall in Hierarchical Navigable Small Worlds
Venue: arXiv cs.IR 2024
Year: 2024

Vector search systems, pivotal in AI applications, often rely on the Hierarchical Navigable Small Worlds (HNSW) algorithm. However, the behaviour of HNSW under real-world scenarios using vectors generated with deep learning models remains under-explored. Existing Approximate Nearest Neighbours (ANN) benchmarks and research typically has an over-reliance on simplistic datasets like MNIST or SIFT1M and fail to reflect the complexity of current use-cases. Our investigation focuses on HNSW's efficacy across a spectrum of datasets, including synthetic vectors tailored to mimic specific intrinsic dimensionalities, widely-used retrieval benchmarks with popular embedding models, and proprietary e-commerce image data with CLIP models. We survey the most popular HNSW vector databases and collate their default parameters to provide a realistic fixed parameterisation for the duration of the paper. We discover that the recall of approximate HNSW search, in comparison to exact K Nearest Neighbours (KNN) search, is linked to the vector space's intrinsic dimensionality and significantly influenced by the data insertion sequence. Our methodology highlights how insertion order, informed by measurable properties such as the pointwise Local Intrinsic Dimensionality (LID) or known categories, can shift recall by up to 12 percentage points. We also observe that running popular benchmark datasets with HNSW instead of KNN can shift rankings by up to three positions for some models. This work underscores the need for more nuanced benchmarks and design considerations in developing robust vector search systems using approximate vector search algorithms. This study presents a number of scenarios with varying real world applicability which aim to better increase understanding and future development of ANN algorithms and embedding
Enhancing Sliding Performance with Aerial Robots: Analysis and Solutions for Non-Actuated Multi-Wheel Configurations
Venue: arXiv cs.RO 2024
Year: 2024

Sliding tasks performed by aerial robots are valuable for inspection and simple maintenance tasks at height, such as non-destructive testing and painting. Although various end-effector designs have been used for such tasks, non-actuated wheel configurations are more frequently applied thanks to their rolling capability for sliding motion, mechanical simplicity, and lightweight design. Moreover, a non-actuated multi-wheel (more than one wheel) configuration in the end-effector design allows the placement of additional equipment e.g., sensors and tools in the center of the end-effector tip for applications. However, there is still a lack of studies on crucial contact conditions during sliding using aerial robots with such an end-effector design. In this article, we investigate the key challenges associated with sliding operations using aerial robots equipped with multiple non-actuated wheels through in-depth analysis grounded in physical experiments. The experimental data is used to create a simulator that closely captures real-world conditions. We propose solutions from both mechanical design and control perspectives to improve the sliding performance of aerial robots. From a mechanical standpoint, design guidelines are derived from experimental data. From a control perspective, we introduce a novel pressure-sensing-based control framework that ensures reliable task execution, even during sliding maneuvers. The effectiveness and robustness of the proposed approaches are then validated and compared using the built simulator, particularly in high-risk scenarios.
A System for Quantifying Data Science Workflows with Fine-Grained Procedural Logging and a Pilot Study
Venue: arXiv cs.HC 2024
Year: 2024

It is important for researchers to understand precisely how data scientists turn raw data into insights, including typical programming patterns, workflow, and methodology. This paper contributes a novel system, called DataInquirer, that tracks incremental code executions in Jupyter notebooks (a type of computational notebook). The system allows us to quantitatively measure timing, workflow, and operation frequency in data science tasks without resorting to human annotation or interview. In a series of pilot studies, we collect 97 traces, logging data scientist activities across four studies. While this paper presents a general system and data analysis approach, we focus on a foundational sub-question in our pilot studies: How consistent are different data scientists in analyzing the same data? We taxonomize variation between data scientists on the same dataset according to three categories: semantic, syntactic, and methodological. Our results suggest that there are statistically significant differences in the conclusions reached by different data scientists on the same task and present quantitative evidence for this phenomenon. Furthermore, our results suggest that AI-powered code tools subtly influence these results, allowing student participants to generate workflows that more resemble expert data practitioners.
Towards robust prediction of material properties for nuclear reactor design under scarce data -- a study in creep rupture property
Venue: arXiv cs.LG 2024
Year: 2024

Advances in Deep Learning bring further investigation into credibility and robustness, especially for safety-critical engineering applications such as the nuclear industry. The key challenges include the availability of data set (often scarce and sparse) and insufficient consideration of the uncertainty in the data, model, and prediction. This paper therefore presents a meta-learning based approach that is both uncertainty- and prior knowledge-informed, aiming at trustful predictions of material properties for the nuclear reactor design. It is suited for robust learning under limited data. Uncertainty has been accounted for where a distribution of predictor functions are produced for extrapolation. Results suggest it achieves superior performance than existing empirical methods in rupture life prediction, a case which is typically under a small data regime. While demonstrated herein with rupture properties, this learning approach is transferable to solve similar problems of data scarcity across the nuclear industry. It is of great importance to boosting the AI analytics in the nuclear industry by proving the applicability and robustness while providing tools that can be trusted.
NUTS, NARS, and Speech
Venue: arXiv cs.LG 2024
Year: 2024

To investigate whether "Intelligence is the capacity of an information-processing system to adapt to its environment while operating with insufficient knowledge and resources", we look at utilising the non axiomatic reasoning system (NARS) for speech recognition. This article presents NUTS: raNdom dimensionality redUction non axiomaTic reasoning few Shot learner for perception. NUTS consists of naive dimensionality reduction, some pre-processing, and then non axiomatic reasoning (NARS). With only 2 training examples NUTS performs similarly to the Whisper Tiny model for discrete word identification.
Sparsity- and Hybridity-Inspired Visual Parameter-Efficient Fine-Tuning for Medical Diagnosis
Venue: arXiv cs.CV 2024
Year: 2024

The success of Large Vision Models (LVMs) is accompanied by vast data volumes, which are prohibitively expensive in medical diagnosis.To address this, recent efforts exploit Parameter-Efficient Fine-Tuning (PEFT), which trains a small number of weights while freezing the rest.However, they typically assign trainable weights to the same positions in LVMs in a heuristic manner, regardless of task differences, making them suboptimal for professional applications like medical diagnosis.To address this, we statistically reveal the nature of sparsity and hybridity during diagnostic-targeted fine-tuning, i.e., a small portion of key weights significantly impacts performance, and these key weights are hybrid, including both task-specific and task-agnostic parts.Based on this, we propose a novel Sparsity- and Hybridity-inspired Parameter Efficient Fine-Tuning (SH-PEFT).It selects and trains a small portion of weights based on their importance, which is innovatively estimated by hybridizing both task-specific and task-agnostic strategies.Validated on six medical datasets of different modalities, we demonstrate that SH-PEFT achieves state-of-the-art performance in transferring LVMs to medical diagnosis in terms of accuracy. By tuning around 0.01% number of weights, it outperforms full model fine-tuning.Moreover, SH-PEFT also achieves comparable performance to other models deliberately optimized for specific medical tasks.Extensive experiments demonstrate the effectiveness of each design and reveal that large model transfer holds great potential in medical diagnosis.
Arithmetic Reasoning with LLM: Prolog Generation & Permutation
Venue: arXiv cs.CL 2024
Year: 2024

Instructing large language models (LLMs) to solve elementary school math problems has shown great success using Chain of Thought (CoT). However, the CoT approach relies on an LLM to generate a sequence of arithmetic calculations which can be prone to cascaded calculation errors. We hypothesize that an LLM should focus on extracting predicates and generating symbolic formulas from the math problem description so that the underlying calculation can be done via an external code interpreter. We investigate using LLM to generate Prolog programs to solve mathematical questions. Experimental results show that our Prolog-based arithmetic problem-solving outperforms CoT generation in the GSM8K benchmark across three distinct LLMs. In addition, given the insensitive ordering of predicates and symbolic formulas in Prolog, we propose to permute the ground truth predicates for more robust LLM training via data augmentation.
Towards Clinical AI Fairness: Filling Gaps in the Puzzle
Venue: arXiv cs.AI 2024
Year: 2024

The ethical integration of Artificial Intelligence (AI) in healthcare necessitates addressing fairness-a concept that is highly context-specific across medical fields. Extensive studies have been conducted to expand the technical components of AI fairness, while tremendous calls for AI fairness have been raised from healthcare. Despite this, a significant disconnect persists between technical advancements and their practical clinical applications, resulting in a lack of contextualized discussion of AI fairness in clinical settings. Through a detailed evidence gap analysis, our review systematically pinpoints several deficiencies concerning both healthcare data and the provided AI fairness solutions. We highlight the scarcity of research on AI fairness in many medical domains where AI technology is increasingly utilized. Additionally, our analysis highlights a substantial reliance on group fairness, aiming to ensure equality among demographic groups from a macro healthcare system perspective; in contrast, individual fairness, focusing on equity at a more granular level, is frequently overlooked. To bridge these gaps, our review advances actionable strategies for both the healthcare and AI research communities. Beyond applying existing AI fairness methods in healthcare, we further emphasize the importance of involving healthcare professionals to refine AI fairness concepts and methods to ensure contextually relevant and ethically sound AI applications in healthcare.
Generative AI Enhances Team Performance and Reduces Need for Traditional Teams
Venue: arXiv cs.HC 2024
Year: 2024

Recent advancements in generative artificial intelligence (AI) have transformed collaborative work processes, yet the impact on team performance remains underexplored. Here we examine the role of generative AI in enhancing or replacing traditional team dynamics using a randomized controlled experiment with 435 participants across 122 teams. We show that teams augmented with generative AI significantly outperformed those relying solely on human collaboration across various performance measures. Interestingly, teams with multiple AIs did not exhibit further gains, indicating diminishing returns with increased AI integration. Our analysis suggests that centralized AI usage by a few team members is more effective than distributed engagement. Additionally, individual-AI pairs matched the performance of conventional teams, suggesting a reduced need for traditional team structures in some contexts. However, despite this capability, individual-AI pairs still fell short of the performance levels achieved by AI-assisted teams. These findings underscore that while generative AI can replace some traditional team functions, more comprehensively integrating AI within team structures provides superior benefits, enhancing overall effectiveness beyond individual efforts.
Proof of Quality: A Costless Paradigm for Trustless Generative AI Model Inference on Blockchains
Venue: arXiv cs.AI 2024
Year: 2024

Generative AI models, such as GPT-4 and Stable Diffusion, have demonstrated powerful and disruptive capabilities in natural language and image tasks. However, deploying these models in decentralized environments remains challenging. Unlike traditional centralized deployment, systematically guaranteeing the integrity of AI model services in fully decentralized environments, particularly on trustless blockchains, is both crucial and difficult. In this paper, we present a new inference paradigm called \emph{proof of quality} (PoQ) to enable the deployment of arbitrarily large generative models on blockchain architecture. Unlike traditional approaches based on validating inference procedures, such as ZKML or OPML, our PoQ paradigm focuses on the outcome quality of model inference. Using lightweight BERT-based cross-encoders as our underlying quality evaluation model, we design and implement PQML, the first practical protocol for real-world NLP generative model inference on blockchains, tailored for popular open-source models such as Llama 3 and Mixtral. Our analysis demonstrates that our protocol is robust against adversarial but rational participants in ecosystems, where lazy or dishonest behavior results in fewer benefits compared to well-behaving participants. The computational overhead of validating the quality evaluation is minimal, allowing quality validators to complete the quality check within a second, even using only a CPU. Preliminary simulation results show that PoQ consensus is generated in milliseconds, 1,000 times faster than any existing scheme.